Plastics manufacturing has made continuous gains in capability and competitiveness. Many industry advancements have been fueled by technological progress related to process analysis, instrumentation, and control. FIG. 1 is a prior art closed-loop injection molding machine 10 with varying levels of feedback. It is generally recognized that feedback about the process may be provided by pressure and temperature sensors 12 disposed on the nozzle, as well as a barrel temperature sensor 14 placed in the machine. In addition, current molding controller technology also relies on machine feedback employing sensors such as a hydraulic pressure sensor 16 placed behind the screw, screw position sensors and screw velocity sensors 18, clamp force sensor 20, limit switches 22, melt pressure and melt temperature flow-rate sensors 24, and others sensors.
Polymer processing provides for the mass production of a wide range of economical yet complex products. In injection molding, thermoplastic feedstock in the form of pellets is melted through conduction and viscous dissipation to form a homogeneous melt. Once a melt is collected, it is forced into a mold to form the desired complex shape. The replication and final dimensions of the molded part relative to the mold cavity is related to the shrinkage of the polymer as it cools inside and outside of the mold. Shrinkage is a complex function of 1) the size, shape, and wall thickness of the part design, 2) the free volume, morphology, and material properties of the polymeric resin, 3) the details of the mold including feed system and cooling system design, and 4) the molding conditions such as flow rates, packing pressures, melt and mold temperatures, timings, etc.
The ability to predict and control shrinkage is directly related to the consistency of the molded part dimensions and the usefulness of the molded part, especially in tight tolerance applications which is often employed. For example, commercial and fine tolerances of 0.3% and 0.15% of the overall length dimension for polycarbonate (PC) is often employed. Material shrinkage is characterized by standard tests including ASTM D955-00 and ISO 294-4. However, these standards are typically applied to a tensile bar with a wall thickness of 3.2 mm and assumed process conditions. As such, the final shrinkage and part dimensions in industry applications may vary substantially from those reported. Product designers, mold designers, and molders employ methods to hedge errors in shrinkage rates, yet standard dimensional tolerances as specified by the Society of the Plastics Industry have not changed in the past thirty years.
Technological capabilities of the industry have improved since 1970 when many plastics molding machines still used open-loop control for most subsystems. Since the advent of programmable logic control, the majority of machine input variables have become individually controlled via single-input single-output PID (proportional-integral-derivative) algorithms. Continuing advances in machine and control system designs have greatly improved the time response and absolute repeatability of the process. Similar advances have been made with respect to mold making and polymer synthesis. As a result, tighter tolerances are possible, albeit with an uncertain amount of testing, instrumentation, and processing costs.
There has been increasing recognition that the measurement and control of the polymer state within the mold cavity is vital to product quality. Accordingly, there has been a proliferation of cavity pressure sensors based on load cells, strain gages, and piezoelectric materials. Concurrently, other methods have been developed for measuring melt temperature in the mold including infrared sensors and thermocouples. Ultrasonic methods have also been developed to detect the presence and solidification of the melt in the mold cavity. These sensors provide valuable information that is commonly used with statistical process control to track the process consistency. However, no single control strategy or system design has been universally successful, and defective components continue to be manufactured during high volume production.
To improve the capability of these sensors to predict quality, sensor fusion approaches have incorporated multiple sensor streams with on-line and/or post-molding analyses to predict the part dimensions. The approaches are most often either mechanistic or statistical. Mechanistic approaches vary in complexity from relatively simple analysis of pressure-volume-temperature relations to complex thermo viscoelastic modeling of residual stress relaxation. Statistical models frequently rely on regression, neural networks, or other methods.
One attempt is that of Anthony Bjur of NIST and Charles Thomas of the University of Utah, who developed an optical fiber sensor inserted into the ejector pin channel of a mold using an ejector pin sleeve with a sapphire window at its end. As shown in FIG. 2, a sapphire window 30 was positioned flush with a wall of the mold 32 having a mold cavity 33. A fiber optic cable 34 is position within an ejector pin 36. The fiber optic cable included a bundle of nineteen 100 micron diameter fibers, seven of which carried light from a helium-neon laser 40 and twelve of which transmitted reflected light back to a silicon photodiode 42. In operation, incident light was transmitted through the resin and then reflected back to the detector from every boundary at which there was a discontinuity in the index of refraction. During the molding cycle, the detected light was analyzed to: 1) detect the arrival of the polymer melt, 2) detect separation of the resin from the mold wall upon shrinkage, and 3) monitor the molded part shrinkage as shown in FIG. 3.
More recently, Fathi et al. designed a glass mold and used a high speed camera to observe the shrinkage during the molding process (S. Fathi and A. H. Behravesh, “Visualization of In-Mold Shrinkage in Injection Molding Process,” Polymer Engineering & Science, vol. 47, pp. 750-756, 2007). Angstadt et al. have also implemented a glass mold to observe the development of birefringence in injection molding (D.C. Angstadt, C. H. Gasparian, J. P. Coulter, and R. A. Pearson, “In-situ observation of birefringence during vibration-assisted injection molding,” SPE ANTEC, vol. 1, pp. 783-787, 2004). The size, cost, and maintenance issues associated with these designs prevent widespread adoption for in-mold shrinkage measurement.
In addition, there have been significant increases in molded part complexity due to the development and widespread implementation of design for manufacturing and assembly (DFMA) guidelines that leverage the capability of the injection molding process. One common DFMA guideline calls for the consolidation of multiple parts whenever possible, which leads to fewer but more complex components. Given such potential functionality arising from complex molded parts, it is currently not uncommon for a molded part, such as an inkjet cartridge, to specify more than thirty critical dimensions with tight tolerances.
There is a need for further sensors and methods for controlling the formation of injected molded parts.